A decoder is configured to decode a plurality of texels from a received block of texture data encoded according to the Adaptive Scalable Texture Compression (ASTC) format, and includes a parameter decode unit configured to decode configuration data for the received block of texture data, a colour decode unit configured to decode colour endpoint data for the plurality of texels of the received block in dependence on the configuration data, a weight decode unit configured to decode interpolation weight data for each of the plurality of texels of the received block in dependence on the configuration data, and at least one interpolator unit configured to calculate a colour value for each of the plurality of texels of the received block using the interpolation weight data for that texel and a pair of colour endpoints from the colour endpoint data. At least one of the parameter decode unit, colour decode unit and weight decode unit are configured to decode intermediate data from the received block that is common to the decoding of at least a subset of texels of that block and to use that decoded intermediate data as part of the decoding of at least two of the plurality of texels from the received block of texture data.

Methods and devices for coding point clouds using direct coding mode to code coordinates of a point within a sub-volume associated with a current node instead of a pattern of occupancy for child nodes. Eligibility for use of direct coding is based on occupancy data from another node. If eligible, then a flag is represented in the bitstream to signal whether direct coding is applied to points in the sub-volume or not.

Methods and devices for coding point clouds using direct coding mode to code coordinates of a point within a sub-volume associated with a current node instead of a pattern of occupancy for child nodes. Eligibility for use of direct coding is based on occupancy data from another node. If eligible, then a flag is represented in the bitstream to signal whether direct coding is applied to points in the sub-volume or not.

An apparatus and method to execute ray tracing instructions. For example, one embodiment of an apparatus comprises execution circuitry to execute a dequantize instruction to convert a plurality of quantized data values to a plurality of dequantized data values, the dequantize instruction including a first source operand to identify a plurality of packed quantized data values in a source register and a destination operand to identify a destination register in which to store a plurality of packed dequantized data values, wherein the execution circuitry is to convert each packed quantized data value in the source register to a floating point value, to multiply the floating point value by a first value to generate a first product and to add the first product to a second value to generate a dequantized data value, and to store the dequantized data value in a packed data element location in the destination register.

An apparatus and method to execute ray tracing instructions. For example, one embodiment of an apparatus comprises execution circuitry to execute a dequantize instruction to convert a plurality of quantized data values to a plurality of dequantized data values, the dequantize instruction including a first source operand to identify a plurality of packed quantized data values in a source register and a destination operand to identify a destination register in which to store a plurality of packed dequantized data values, wherein the execution circuitry is to convert each packed quantized data value in the source register to a floating point value, to multiply the floating point value by a first value to generate a first product and to add the first product to a second value to generate a dequantized data value, and to store the dequantized data value in a packed data element location in the destination register.

A computer-implemented method comprises receiving a first compressed representation of a texture map in a first compression format, wherein the first compressed representation has been compressed using a first compressor, and receiving an array of compression parameters for a second compressor, the array of compression parameters including one or more respective compression parameters for each of a plurality of pixel regions of the texture map. The method further comprises decompressing the first compressed representation of the texture map to obtain the texture map, and compressing, using the second compressor, the texture map to a second compressed representation in a second compression format, comprising compressing each of said plurality of pixel regions of the texture map in accordance with the respective one or more compression parameters. The method further comprises storing the second compressed representation of the texture map to one or more memories accessible by a graphics processing unit, and selectively decompressing portions of the second compressed representation of the texture map using the graphical processing unit.

A computer-implemented method comprises receiving a first compressed representation of a texture map in a first compression format, wherein the first compressed representation has been compressed using a first compressor, and receiving an array of compression parameters for a second compressor, the array of compression parameters including one or more respective compression parameters for each of a plurality of pixel regions of the texture map. The method further comprises decompressing the first compressed representation of the texture map to obtain the texture map, and compressing, using the second compressor, the texture map to a second compressed representation in a second compression format, comprising compressing each of said plurality of pixel regions of the texture map in accordance with the respective one or more compression parameters. The method further comprises storing the second compressed representation of the texture map to one or more memories accessible by a graphics processing unit, and selectively decompressing portions of the second compressed representation of the texture map using the graphical processing unit.

The present disclosure provides a UAV video aesthetic quality evaluation method based on multi-modal deep learning, which establishes a UAV video aesthetic evaluation data set, analyzes the UAV video through a multi-modal neural network, extracts high-dimensional features, and concatenates the extracted features, thereby achieving aesthetic quality evaluation of the UAV video. There are four steps, step one to: establish a UAV video aesthetic evaluation data set, which is divided into positive samples and negative samples according to the video shooting quality; step two to: use SLAM technology to restore the UAV's flight trajectory and to reconstruct a sparse 3D structure of the scene; step three to: through a multi-modal neural network, extract features of the input UAV video on the image branch, motion branch, and structure branch respectively; and step four to: concatenate the features on multiple branches to obtain the final video aesthetic label and video scene type.

Techniques of compressing level of detail (LOD) data involve sharing a texture image LOD among different mesh LODs for single-rate encoding. That is, a first texture image LOD corresponding to a first mesh LOD may be derived by refining a second texture image LOD corresponding to a second mesh LOD. This sharing is possible when texture atlases of LOD meshes are compatible.

Techniques of compressing level of detail (LOD) data involve sharing a texture image LOD among different mesh LODs for single-rate encoding. That is, a first texture image LOD corresponding to a first mesh LOD may be derived by refining a second texture image LOD corresponding to a second mesh LOD. This sharing is possible when texture atlases of LOD meshes are compatible.